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Design and development of humanoid robotic arm

10.11591/ijra.v14i1.pp11-18
Shripad Bhatlawande , Sakshi Kulkarni , Shajjad Shaikh , Sachi Kurian , Swati Shilaskar
This paper presents the design, development, and evaluation of a 5-degrees of freedom (5-DoF) humanoid robotic arm featuring a sophisticated 5-finger gripper. The five degrees of freedom include the base, shoulder, elbow, wrist, and gripper, all controlled by MG996R servo motors to enhance grasping, positioning, flexibility, and mobility. The arm is constructed from laser-cut aluminum sheets. It effectively picks and places objects such as bottles and bags. A high-speed portable computing system is used to control robotics hand operations. A webcam is used for object detection and to acquire information about the surroundings. The system uses a convolutional neural network-based MobileNet architecture for object detection. The robotic hand is used as an assistive aid for amputees. It mimics finger movements based on detected objects. The system achieved a precision of 0.97 for bags and 0.93 for bottles, with accuracies of 96.83% and 92.42%, respectively. The system employs advanced computer vision algorithms and real-time strategies, ensuring adaptability across various tasks. It integrates advanced visual systems and improved feedback to enhance user interaction and overall usability. It addresses trade-offs between detection precision and processing time.
Volume: 14
Issue: 1
Page: 11-18
Publish at: 2025-03-01

Visual treatment with AR for children with dysphasia

10.11591/ijeecs.v37.i3.pp1702-1711
Misael Lazo Amado , Meyluz Monica Paico Campos
The language disorder known as dysphasia significantly affects the ability to communicate effectively, presenting challenges in both comprehension and expression of language. To address this issue, the development of a visual treatment using augmented reality (AR) specifically designed for children with dysphasia has been proposed. The methodology selected for this project is analysis, design, development, implementation, and evaluation (ADDIE), an innovative methodology that encompasses analysis, design, development, implementation and evaluation. This methodology is perfectly adapted to the needs of the project, allowing a systematic and complete approach at all stages of the process of creating the visual treatment. The results obtained show that the visual treatment with AR has been positively evaluated by development experts and dysphasia specialists. Its innovative capacity to assist children with this disorder in health and educational settings is highlighted. This approach provides an effective tool to improve the communication and language development of children affected by dysphasia, offering new opportunities for their learning and growth. Its implementation in healthcare and educational settings could have a significant impact on the quality of life and development of these children.
Volume: 37
Issue: 3
Page: 1702-1711
Publish at: 2025-03-01

VotTomNet: Voting-based tomato disease diagnosis with transfer learning

10.11591/ijra.v14i1.pp38-46
Shradha Joshi-Bag , Wani V. Patil , Shrikant Chavate
The research presents an advanced automation system, termed VotTomNet, designed for diagnosing tomato leaf diseases using transfer learning, and soft and hard voting ensemble techniques. By leveraging six pre-trained deep learning convolutional neural networks—VGG16, InceptionNet, ResNet, MobileNet, EfficientNet, and DenseNet—the system achieved an impressive accuracy of 99.2%. These models were meticulously fine-tuned to diagnose multiple types of tomato diseases with heightened precision. The integration of a soft and hard voting mechanism further enhanced the overall diagnostic accuracy by combining the strengths of these diverse models into a powerful ensemble. The findings underscore the robustness, reliability, and effectiveness of this ensemble technique, marking a significant advancement in precision agriculture and crop health assessment. By outperforming traditional methods, this approach offers a more practical and efficient solution for large-scale agricultural applications, enabling comprehensive crop management and improved yield. In conclusion, this research lays a strong foundation for future innovations in automated plant disease diagnosis and agricultural technology. Its contributions have the potential to revolutionize disease management, reduce crop losses, and ultimately enhance food security on a global scale.
Volume: 14
Issue: 1
Page: 38-46
Publish at: 2025-03-01

Optimization model for endurance performance of electric rotorcraft transport drones and its application prospects

10.11591/ijra.v14i1.pp129-142
Huang Zihan , Long Bo , Li Jiyu
The operational parameter configuration and performance optimization of electric rotorcraft transport unmanned aerial vehicles (UAVs) currently lack comprehensive guiding theory, impacting UAV endurance and efficiency, thereby limiting industry growth. This paper analyzes factors affecting UAV endurance and establishes a hover endurance model for electric rotorcraft transport UAVs through theoretical derivation and testing. Based on this model, we introduce the concepts of thrust redundancy coefficient and load cut-off line, proposing an optimal endurance configuration theory. This theory categorizes the parameter configuration range into light load, ideal configuration, load cut-off, and endurance saturation zones. Using current operational parameters, we evaluate and optimize UAV performance. Verification results demonstrate high model accuracy, with error rates ranging from 1.89% to 5.69%. After optimization, the payload capacities of two transport UAVs increased by 6.25%, and their endurance improved by 6.97% and 9.5%, respectively, enhancing overall efficiency. This model provides a solid framework for assessing endurance capabilities and offers targeted optimization suggestions, making it crucial for improving UAV performance.
Volume: 14
Issue: 1
Page: 129-142
Publish at: 2025-03-01

Position and orientation analysis of Jupiter robot arm for navigation stability

10.11591/ijra.v14i1.pp1-10
Omar Shalash , Adham Sakr , Yasser Salem , Ahmed Abdelhadi , HossamEldin Elsayed , Ahmed El-Shaer
Jupiter robot has made a great impact in the educational field with its support for autonomous navigation, visual perception, and many other features from its artificial intelligence platform's learning box. This study undertakes a kinematic model design of Jupiter's arm to aid the robot's motion stability. This process involved the determination of a homogeneous transformation matrix, followed by the determination of orientation, position, and Euler angles. Ultimately, the homogeneous transformation matrix was successfully derived, and the simplification of direct kinematic matrices was achieved. Consequently, the kinematic analysis for Jupiter's arm was established using the position Denavit–Hartenberg method, orientation, and Euler angles, proving to be valuable in the context of this research.
Volume: 14
Issue: 1
Page: 1-10
Publish at: 2025-03-01

Performance comparison of optical flow and background subtraction and discrete wavelet transform methods for moving objects

10.11591/ijra.v14i1.pp93-102
Monika Sharma , Kuldeep Singh Kaswan , Dileep Kumar Yadav
Self-driving cars and other autonomous vehicles rely on systems that can recognize and follow objects. The ways help people make safe decisions and navigate by showing things like people, cars, obstacles, and traffic lights. Computer vision algorithms encompass both object detection and tracking. Different methods are specifically developed for picture or video analysis not only to identify items within the visual content but also to accurately determine their precise locations. This can operate independently as an algorithm or as a constituent of an item-tracking system. Object tracking algorithms can be used to follow objects over video frames, providing a contrasting approach. The research article focuses on the mathematical model simulation of optical flow, background subtraction, and discrete wavelet transform (DWT) methods for moving objects. The performance evaluation of the methods is done based on simulation response time, accuracy, sensitivity, and specificity doe several images in different environments. The DWT has shown optimal behavior in terms of the response time of 0.27 seconds, accuracy of 95.34 %, selectivity of 95.96 %, and specificity of 94.68 %.
Volume: 14
Issue: 1
Page: 93-102
Publish at: 2025-03-01

Analyzing the effectiveness of waiting times at the pharmacy of the King Hamad University Hospital

10.11591/ijra.v14i1.pp121-128
Anwar Al-Mofleh , Mohamed Alseddiqi , Osama Najam , Budoor AlMannaei , Leena Albalooshi
This study investigates the efficiency of waiting times at the pharmacy of King Hamad University Hospital, with a primary focus on optimizing patient experiences. Efficient pharmacy services are vital for ensuring the timely provision of medications to patients. Prolonged waiting times not only affect patient satisfaction but may also have implications for overall healthcare outcomes. To assess the effectiveness of waiting times, we conducted comprehensive analysis, including data collection, surveys, and observations. Our findings reveal valuable insights into the current state of pharmacy operations and the patient experience. We explore factors contributing to waiting times, such as prescription processing, queue management, and staff allocation. Through this analysis, we aim to provide actionable recommendations to enhance pharmacy efficiency, reduce waiting times, and improve patient satisfaction. Our study underscores the importance of optimizing pharmacy operations to ensure that patients receive timely and high-quality healthcare services. By addressing these issues, King Hamad University Hospital can not only enhance the overall patient experience but also contribute to better healthcare outcomes and increased operational efficiency. This research serves as a valuable resource for healthcare administrators, policymakers, and practitioners seeking to improve pharmacy services and patient satisfaction in hospital settings.
Volume: 14
Issue: 1
Page: 121-128
Publish at: 2025-03-01

Attention deficit and hyperactivity disorder classification in quantitative EEG signals using machine learning algorithms

10.11591/ijeecs.v37.i3.pp1580-1587
Syifani Ihfadza Aliyah , Sastra Kusuma Wijaya , Yetty Ramli
Attention deficit and hyperactivity disorder (ADHD) classification method as a quantitative observation has been continually improved to assist medical practitioners. Currently, machine learning algorithms such as k-nearest neighbors (KNN), multilayer perceptron (MLP), and support vector machine (SVM) are widely used. This study proposed a feature extraction method for quantitative electroencephalography (qEEG) data derived from the continuous wavelet transform (CWT) to classify children with ADHD versus healthy subjects. Subsequently, this study compared the performance of the classification pipeline before and after the implementation of principal component analysis (PCA) on the features prior to processing with machine learning algorithms. The results revealed that the overall performance of the classifiers consistently improved after the implementation of PCA. The results highlight the varying impact of PCA on classifier performance, with KNN showing an improvement in testing accuracy from 61.84% to 69.21% following PCA implementation, while the other classifiers showed deterioration in performance. These findings suggest that while PCA may be beneficial for some classifiers, its impact on performance varies depending on the specific characteristics of the dataset and the classifier utilized. Moreover, this study provides insight for future implementation of the classification method for ADHD patients across a more specific clinical range of the spectrum.
Volume: 37
Issue: 3
Page: 1580-1587
Publish at: 2025-03-01

Comparative analysis of incremental conductance MPPT for enhanced algorithm performance

10.11591/ijeecs.v37.i3.pp1415-1427
Asnil Asnil , Refdinal Nazir , Krismadinata Krismadinata , Muhammad Nasir
The incremental conductance (INC) approach is limited in terms of its response speed, accuracy during steady state, and ability to handle oscillations. As a result, this algorithm is ineffective in situations with variations in solar radiation and temperature, particularly abrupt fluctuations. An enhanced variable step size INC approach is suggested to improve system efficiency and performance. The proposed algorithm is subjected to rigorous testing and analysis with other INC methods for better results. The research findings indicate that the proposed algorithm's tracking efficiency can be enhanced to 99.83% under varying radiation conditions and constant temperature. Additionally, the method utilizing the second model achieves a 99.83% tracking efficiency, while the method employing the first model achieves a tracking efficiency of 89.99%. In comparison, the conventional method achieves a tracking efficiency of 96.36%. Regarding radiation and temperature circumstances, the tracking efficiency value varies for different methods. Specifically, the tracking efficiency is 95.21% for the approach using the proposed algorithm’s, 92.94% for the method using the second model, 83,39% for the method using the first model, and 91.19% for the conventional way. Therefore, it can be inferred that the suggested Maximum power point tracking (MPPT) algorithm performs better than other algorithms under both test situations.
Volume: 37
Issue: 3
Page: 1415-1427
Publish at: 2025-03-01

Design and development of an Arduino-based oxygen saturation, heart rate variability, and blood glucose measurement device

10.11591/ijra.v14i1.pp74-81
Yehezkiel P. Putra , Wahyu K. Wibowo , Soni Prayogi
This study presents the design and development of an Arduino-based device for measuring oxygen saturation, heart rate variability (HRV), and blood glucose levels. The primary goal is to create an affordable, portable, and accurate health monitoring solution suitable for home health care and remote medical services. The device integrates a pulse oximeter sensor to measure oxygen saturation and heart rate, an electrocardiogram (ECG) sensor for capturing HRV data, and a non-invasive sensor for regular blood glucose monitoring, all managed by an Arduino microcontroller. Data collected by the sensors is processed and displayed on a user-friendly interface, enabling real-time tracking of health metrics. The device's performance was rigorously tested and validated against standard medical equipment, demonstrating comparable accuracy in measuring the targeted health parameters. This innovative solution aims to enhance personal health monitoring, reduce the burden on healthcare systems, and promote early detection and better management of health conditions. The affordability and ease of use make this device accessible to a wider population, potentially improving health outcomes. Future developments will focus on refining sensor accuracy and expanding the device's capabilities to monitor additional health metrics.
Volume: 14
Issue: 1
Page: 74-81
Publish at: 2025-03-01

Development of newton dynamometer instrumentation integrated with smart counter applications based on Hooke's law

10.11591/ijeecs.v37.i3.pp1506-1514
Yulkifli Yulkifli , Alwi Nofriandi , Washilla Audia , Asrizal Asrizal , Yenni Darvina , Afdhal Muttaqin , Rosly Jaafar
This research presents the development of an instrumentation system that employs ultrasonic sensors for Newton dynamometer applications. A key parameter measured is the change in spring length before and after loading. The methodology implemented in this study is based on Hooke's Law, applied within the instrumentation devices. The length change data is transmitted to a smartphone via a Bluetooth module integrated into the instrument. This allows for flexible data usage and input through a calculator-based application created with MIT App Inventor, tailored to the relevant supporting parameters. Before implementation, the sensors underwent characterization to assess the linearity of their output compared to a standard measuring tool, specifically a ruler. The linearity test yielded a coefficient of 0.9998, indicating excellent performance for this application. Additionally, the system achieved an average accuracy of 94.12% and an average precision of 99.94%.
Volume: 37
Issue: 3
Page: 1506-1514
Publish at: 2025-03-01

A novel dataset and part-of-speech tagging approach for enhancing sentiment analysis in Kannada

10.11591/ijeecs.v37.i3.pp1661-1671
Sunil Mugalihalli Eshwarappa , Vinay Shivasubramanyan
The problem addressed in this research is the limited availability of labelled datasets and effective sentiment analysis tools for the Kannada language. Existing challenges include linguistic variations, cultural diversities, and the absence of comprehensive datasets designed specifically for sentiment analysis in Kannada. This research aims to enhance sentiment analysis capabilities for the Kannada language, addressing challenges posed by linguistic variations and limited labelled datasets. A novel Kannada dataset derived from SemEval 2014 task 4 was created using a conversion process. The dataset was processed using part-of-speech tagging, and a specialized model called K-BERT (Kannada bidirectional encoder representations from transformers) was introduced and implemented using Python within the Anaconda environment. Performance evaluation results showcased K-BERT's superiority over traditional machine learning (ML) algorithms and the BERT model, achieving an accuracy of 0.98, precision of 0.97, recall of 0.97, and F-score of 0.98 in sentiment classification for Kannada text data. This work contributes a unique Kannada dataset, introduces the K-BERT model specifically designed for Kannada sentiment analysis, and emphasizes the importance of collaborative efforts in advancing natural language processing (NLP) research for multilingual environments.
Volume: 37
Issue: 3
Page: 1661-1671
Publish at: 2025-03-01

Leveraging 3D convolutional networks for effective video feature extraction in video summarization

10.11591/ijeecs.v37.i3.pp1616-1625
Bhakti Deepak Kadam , Ashwini Mangesh Deshpande
Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation.
Volume: 37
Issue: 3
Page: 1616-1625
Publish at: 2025-03-01

A hybrid approach for hotspot problem using load balancing and advanced ant colony algorithm

10.11591/ijeecs.v37.i3.pp1865-1873
Padmini Mysuru Srikantha , Sampath Kuzhalvaimozhi , Susheela Shimoga Balasubramanya , Sklita Buthello , Sadana Hulugundi Jagadish , Spandana Mallegowdanakoppalu Thammanna
Wireless sensor networks (WSNs) are crucial in various applications such as environmental surveillance, military operations, transportation monitoring, and healthcare. However, due to a finite set of sensor nodes' resources concerning energy, memory, disk, and CPU processing, nodes in WSNs often face hotspot issues. The sensor nodes that are located near the base station, are responsible for relaying data not only from themselves but also from neighboring nodes. This leads to hotspot issues, where nodes near the base station experience higher traffic loads and faster energy depletion. This paper mainly focuses on mitigating hotspot issues in heterogeneous WSNs using unequal clustering, load balancing, and an advanced ant colony algorithm. This approach involves devising strategies for selecting cluster heads, determining clusters optimal number and formation, and optimizing data transmission processes. Central to the methodology is utilizing load balancing mechanisms and an advanced ant colony algorithm to distribute the workload among sensor nodes more evenly and find the optimum routing path. The proposed algorithm shows promise in alleviating traffic congestion and energy depletion and provides an innovative approach to enhance network performance and prolong the lifespan of sensor nodes.
Volume: 37
Issue: 3
Page: 1865-1873
Publish at: 2025-03-01

Tomato leaf disease detection using Taguchi-based Pareto optimized lightweight CNN

10.11591/ijeecs.v37.i3.pp1772-1784
Bappaditya Das , C. S. Raghuvanshi
The prospect of food security becoming a global danger by 2050 due to the exponential growth of the world population. An increase in production is indispensable to satisfy the escalating demand for food. Considering the scarcity of arable land, safeguarding crops against disease is the best alternative to maximize agricultural output. The conventional method of visually detecting agricultural diseases by skilled farmers is time-consuming and vulnerable to inaccuracies. Technology-driven agriculture is an integral strategy for effectively addressing this matter. However, orthodox lightweight convolutional neural network (CNN) models for early crop disease detection require fine-tuning to enhance the precision and robustness of the models. Discovering the optimal combination of several hyperparameters might be an exhaustive process. Most researchers use trial and error to set hyperparameters in deep learning (DL) networks. This study introduces a new systematic approach for developing a less sensitive CNN for crop leaf disease detection by hyperparameter tuning in DL networks. Hyperparameter tuning using a Taguchi-based orthogonal array (OA) emphasizes the S/N ratio as a performance metric primarily dependent on the model’s accuracy. The multi-objective Pareto optimization technique accomplished the selection of a robust model. The experimental results demonstrated that the suggested approach achieved a high level of accuracy of 99.846% for tomato leaf disease detection. This approach can generate a set of optimal CNN models’ configurations to classify leaf disease with limited resources accurately.
Volume: 37
Issue: 3
Page: 1772-1784
Publish at: 2025-03-01
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